In June 2024, she received a Doctor of Science degree from East China Normal University. Subsequently, in July 2024, she began postdoctoral research at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. Her primary research focuses on nonparametric statistical inference, high-dimensional data analysis, and functional data analysis.
Shuang Dai
/ Doctor
E-mail：daishuang AT swufe.edu.cn
Awards and Honors
Research Projects
- 2025.06 – 2026.06: China Postdoctoral Science Fund, “Sufficient Dimension Reduction via Machine Learning: Theoretical Foundations and Methodological Advances”
Selected Papers
- Chen, B., Dai, S., & Yu, Z. (2025+). Intrinsic minimum average variance estimation for dimension reduction with symmetric positive definite matrices and beyond. Statistica Sinica, in press.
- Dai, S., Ming, J., & Yu, Z. (2025). A distributed minimum average variance estimation for sufficient dimension reduction. Statistics and Its Interface, 18, 315-332.
- Dai, S., Wu, P., & Yu, Z. (2024). New forest-based approaches for sufficient dimension reduction. Statistics and Computing, 34, 176.
- Dai, S., & Huang, Z. (2020). Nonparametric inference for covariate-adjusted model. Statistical and Probability Letters, 162, 108766.
- Dai, S., & Huang, Z. (2019). Estimation for varying coefficient partially nonlinear models with distorted measurement errors. Journal of the Korean Statistical Society, 48, 117-133.